This involves involves one or more of:
- selecting and using methods,
- demonstrating knowledge of concepts and terms,
- communicating using appropriate representations.

This involves involves one or more of:
- selecting and carrying out a logical sequence of steps,
- connecting different concepts or representations,
- demonstrating understanding of concepts;

and also relating findings to a context or communicating thinking using appropriate statements.

This involves involves one or more of:
- devising a strategy to investigate or solve a problem,
- identifying relevant concepts in context'
- developing a chain of logical reasoning,
- making a generalisation;

and also where appropriate, using contextual knowledge to reflect on the answer.

Problems are situations that provide opportunities to apply knowledge or understanding of mathematical and statistical concepts and methods.
Situations will be set in real-life or statistical contexts.
(Well there's a surprise)

Probabilities may be expected to be calculated from formulae, a probability distribution table or graph,
tables of counts or proportions, simulation results or from written information. Candidates should
clearly show the method they have used to calculate probabilities and state assumptions made.

Candidates may be required to interpret solutions in context.
Sensible rounding is expected. Early rounding may be penalised.

Students should know the three different types of chance situations which can arise

Good model: An example of this is the standard theoretical model for a fair coin toss where heads and tails are equally likely with probability ½ each. Repeated tosses of a fair coin can be used to estimate the probabilities of heads and tails. For a fair coin we would expect these estimates to be close to the theoretical model probabilities.

No model: In this situation there is no obvious theoretical model, for example, a drawing pin toss. Here we can only estimate the probabilities and probability distributions via experiment. (These estimates can be used as a basis for building a theoretical model.)

Poor model: In some situations, however, such as spinning a coin, we might think that the obvious theoretical model was equally likely outcomes for heads and tails but estimates of the outcome probabilities from sufficiently large experiments will show that this is a surprisingly poor model. (Try it! Another example is rolling a pencil.) There is now a need to find a better model using the estimates from the experiments.
Link to statistical investigations: Students are exploring outcomes for single categorical variables in statistical investigations from a probabilistic perspective.